Although it is motivating and suitable to design a shallow network, such networks are likely to fall behind in performance. Deep networks have a broader field of vision at the later layers and thus they can make much complex inferences. This ability to analyze a larger portion of the complex featuremaps comes at the cost of network complexity. However, in recent times a few approaches have been proposed to mitigate this. MultiRes blocks [1]} can be used interchangeably with convolutional layers, providing a wider receptive field through multiresolution analysis, in a compact form. In terms of pooling, Spatial Pyramid Pooling (SPP) [2]} can be used to leverage pooling operations from different points of view, as if they were computed at much deeper layers.
